Computational Prediction Of Intrinsic Disorder In Protein .
Computational Prediction of Intrinsic Disorder in ProteinSequences with the disCoP Meta‐predictorChristopher J. Oldfield1, Xiao Fan2, Chen Wang3, A. Keith Dunker4 and Lukasz Kurgan1*1Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA2Department of Pediatrics, Columbia University, New York, NY, 10032, USA3Department of Medicine, Columbia University, New York, NY, 10032, USA4Center for Computational Biology and Bioinformatics, Department of Biochemistry and MolecularBiology, Indiana University School of Medicine, Indianapolis, IN 46202, USA*Corresponding author: lkurgan@vcu.eduAbstractIntrinsically disordered proteins are either entirely disordered or contain disordered regions in theirnative state. These proteins and regions function without the prerequisite of a stable structure andwere found to be abundant across all kingdoms of life. Experimental annotation of disorder lags behindthe rapidly growing number of sequenced proteins, motivating the development of computationalmethods that predict disorder in protein sequences. DisCoP is a user‐friendly webserver that providesaccurate sequence‐based prediction of protein disorder. It relies on meta‐architecture in which theoutputs generated by multiple disorder predictors are combined together to improve predictiveperformance. The architecture of disCoP is presented and its accuracy relative to several other disorderpredictors is briefly discussed. We describe usage of the web interface and explain how to access andread results generated by this computational tool. We also provide an example of prediction resultsand interpretation. The disCoP’s webserver is publicly available dsIntrinsically disordered proteins; IDP; bioinformatics; webserver; meta‐architecture.1. IntroductionIntrinsically disordered proteins (IDPs) form broad structural ensembles and lack stable foldedstructure in isolation under physiological conditions [16, 18, 29, 46, 86, 95]. These proteins have alsobeen called partially folded, natively denatured, natively unfolded, natively disordered, intrinsicallyunstructured, intrinsically denatured, and intrinsically unfolded [16]. IDPs have one or moreintrinsically disordered regions (IDRs) and in some cases they are fully disordered. Recentcomputational studies estimate that eukaryotic organisms have between 3% and 17% of fullydisordered proteins, and that between 30% and 50% of proteins in their proteomes have at least onelong IDR (30 or more consecutive amino acid residues long) [19, 68, 73, 78, 94, 101, 108]. IDPs alsooccupy a large part of proteomes in bacteria, archaea and viruses [5, 19, 24, 50, 78, 92, 94, 99, 101,
102, 104]. They are instrumental for numerous cellular functions including signaling [17, 22, 28, 88],regulation of transcription [26, 48], translation [75], chromatin condensing [20, 53, 74, 83], andmolecular interactions with proteins and nucleic acids [6, 9, 10, 21, 27, 31, 76, 85, 97], to name just afew. Intrinsic disorder was shown to be enriched in alternatively spliced regions [3, 40, 82, 111] and inpost‐translational modification sites [43, 98, 111]. Moreover, IDPs are being explored as drug targets[8, 35], which is motivated by their association with a number of human diseases [57, 87].Sequences of IDRs are substantially different from the sequences of structured regions and proteins.For example, IDRs are enriched in polar amino acids, depleted in large hydrophobic and aromaticamino acid, and have relatively low sequence complexity [4, 45, 80, 81]. These differences underlie thedevelopment of accurate computational methods for the prediction of disorder in protein chains. Over70 computational disorder predictors were developed over the last few decades [2, 11, 14, 15, 25, 32,44, 47, 54, 55, 65, 66, 79]. Many of the recently published methods rely on meta‐architectures thatcombine outputs produced by several disorder predictors to (re)predict disorder. The meta‐predictorsinclude (in chronological order) VSL2 [70], metaPrDOS [37], PreDisorder [12], NN‐CDF [103], MD [84],PONDR‐FIT [100], MFDp [61], CSpritz [90], MetaDisorder [41], ESpritz [89], MFDp2 [60, 63], DisMeta[36], disCoP [23], DISOPRED3 [38], and MobiDB‐lite [67]. This type of predictive architecture ismotivated by studies that empirically demonstrate that outputs from the meta‐predictors are moreaccurate when compared to the results produced by their input single predictors [23, 72]. However,the improved accuracy comes at a cost of a longer runtime and inconvenience. The long runtime stemsfrom the fact that multiple disorder predictions have to be computed and combined together. Theinconvenience is due to the fact that outputs of several disorder predictors must be collected by theuser. The latter drawback is alleviated by some meta‐predictors that incorporate computation of theinput disorder predictors into their publicly available implementations.A recently published example of a convenient meta‐predictor is disCoP (disorder Consensus‐basedPredictor) [23]. The disCoP method is available as a user‐friendly webserver that automates the entireprediction process. Users only need to enter the sequence of their proteins and click the “Run” buttonto obtain disorder prediction. Moreover, benchmarking tests show that DisCoP provides accuratepredictions, with area under the receiver operating characteristic (ROC) curve (AUC) 0.85 andMatthews correlation coefficient (MCC) 0.50. DisCoP was compared empirically to 20 other disorderpredictors including several meta‐predictors such as ESpritz (AUC 0.83 and MCC 0.48), CSpritz (AUC 0.83 and MCC 0.45), MD (AUC 0.82 and MCC 0.45), MFDp (AUC 0.82 and MCC 0.45) andPONDR‐FIT (AUC 0.78 and MCC 0.41). These tests concluded that predictive performance of disCoPis statistically significantly better (p‐value 0.01) [23]. To sum up, the two main advantages of disCoPare the availability of the convenient webserver and good predictive performance.This chapter describes the underlying meta‐architecture of disCoP, explains its web interface andprovides detailed instructions on how to generate predictions with this computational tool. We alsoexplain how to read and interpret the results generated by this meta‐predictor using a case study thatconcerns prediction of intrinsic disorder for the chromatin accessibility complex 16kD protein.2. Materials1. Sequences of proteins to be predicted. The sequences must be formatted using the FASTA format(see Note 1). Up to 5 protein sequences can be submitted at one time as either a file upload or using atext entry field (see Note 2).
2. disCoP: The webserver that is freely available at http://biomine.cs.vcu.edu/servers/disCoP/ isdesigned to be simple to use. All computations are performed on the server side and thus the onlyrequirements for submitting predictions are: an internet connection and a modern web browser(Firefox, Internet Explorer, or Chrome). The webserver visualizes the results directly in the webbrowser window and also delivers these results to the user‐provided email address.INPUT SEQUENCEMG E P R S Q P P V E R P P T A E T F L P L S R V R T I M K S S MD T G L I T N E V L F L M T K C T E L F V R H L A G A STAGESPINE‐DPropensity fordisorderPutative ustPropensity fordisorderPutative ED2Propensity fordisorderPutative ensity fordisorderPutative 0.500.470.410.410.350.400.380.430.420.470.561 STAGE2STAGEBinomial deviance 3Propensity fordisorderPutative IDRs Fig. 1 Prediction process implemented in the disCoP predictor. The outputs of the four disorder predictors (SPINE‐D,DISOclust, DISOPRED2 and MD) generated in stage 1 include the propensity scores and the corresponding putative IDRs,which are show using the green horizontal bars. The dashed boxes with gray shading denote the sliding windows that areused to compute the seven features in stage 2. In stage 3, the binomial deviation regression model predicts the putativepropensities for disorder from the seven features. The putative IDRs generated by disCOP are shown at the bottom of thefigure and they correspond to the residues for which the putative propensities for disorder 0.5. The example showsresults produced for the chromatin accessibility complex 16kD protein (UniProt id: Q9V452)The meta‐architecture of the disCoP’s webserver is shown in Fig. 1. The input protein sequence goesthrough a three‐stage process to generate putative IDRs. In stage 1, the sequence is processed by fourdisorder predictors: SPINE‐D [110], DISOclust [49], DISOPRED2 [93] and MD [84]. This collection of fourpredictors was selected from among 20 disorder predictors using an empirical procedure that aims tomaximize predictive performance [23]. Each of the four methods outputs numeric propensity fordisorder and binary disorder annotations (disordered vs. ordered) for each residue in the input proteinchain. In stage 2, these predictions are processed to produce features that numerically quantifyinformation which is relevant for the disorder prediction. The features are calculated using slidingwindows that aggregate and summarize putative disorder information among neighboring (in thesequence) amino acid residues. This reduces the risk of making spurious predictions. The windows arerepresented by dashed boxes in Fig. 1. A balanced and complementary set of seven features iscollected by considering both types of outputs (propensities and binary) generated by each of the fourdisorder predictors. Stage 3 uses these features as input to a trained regression model to producedisCoP’s predictions in the form of numeric propensities for disorder. These propensities rangebetween 0 and 1, where higher propensity scores are indicative of a higher likelihood of intrinsicdisorder. The disCoP’s webserver further processes these propensities to generate binary predictions,
which correspond to the putative IDRs. Residues with propensities 0.5 are predicted to be disorderedwhile the remaining residues are predicted as ordered/structured (see Note 3).Fig. 2. The disCoP prediction submission webpage. Orange/yellow circles indicate the three steps to submit sequences forpredictions, discussed in the text.3. MethodsSubmission of predictions is made at the main disCoP’s webpage athttp://biomine.cs.vcu.edu/servers/disCoP/. Notification of completed predictions are given by email,
and thus an email address is required for each submission. These notifications provide a link toprediction results, which can be viewed in a browser window and/or downloaded as a parsable textfile. The predictions can be accessed at a later time and they are kept on the webserver for at leastthree months.3.1. Running disCoPThree easy steps are required to submit sequences for prediction (Fig. 2, labels 1, 2, and 3):Step 1. Enter FASTA formatted sequences (see Note 1) in one of two ways: Upload a file of FASTA formatted sequences. Input the FASTA formatted sequences into the white text entry field. This can be doneusing the copy and paste function. An example of properly formatted sequence can beobtained by clicking the “Example” button located below the text entry field.Clicking the “Reset sequence(s)” button clears both submission options. There are limits to boththe number of sequences and maximum length of sequences that can be submitted forprediction (see Note 2).Step 2. Provide an email address (see Note 4). This email is only used to send notification ofcompleted predictions.Step 3. Click “Run disCoP” to start the prediction.Clicking “Run disCoP” takes the user to a status page that reports on the current state of the submittedprediction. Submissions to several different bioinformatics webservers located at thehttp://biomine.cs.vcu.edu site (see Note 5) are entered into the same queue system (see Note 6). Thestatus page reports the current position in the queue and shows when prediction for this submissionbegins. The runtime needed to complete prediction for an average length protein sequence (about 250amino acids) is approximately 10 minutes. The prediction can take over 40 minutes when submitting 5longer protein sequences. After the prediction is completed, the status page automatically redirectsthe user to the prediction results page. This also triggers an email with the location of the results pagethat is sent to the user‐provided email address. There is no need to keep the status page open whilepredictions are running since the notification email is always sent when the prediction is finished.Predictions for disCoP job id: 20190404185835 are ready.Upon the usage the users are requested to use the following citation(s):Fan X, Kurgan LA, 2014. Accurate prediction of disorder in protein chains with a comprehensive and empiricallydesigned consensus. Journal of Biomolecular Structure and Dynamics, 32(3): 448‐464.You can find the results for this job 90404185835/results.htmlThe CSV file can be found here: 04185835/results.csvThe webserver can be found here: http://biomine.cs.vcu.edu/servers/disCoP/Thank you for using our webserver,Biomine groupFig. 3. The disCoP notification email. The email provides unique job identifier and links to the results indicated withorange/yellow circles, discussed in the text.
3.2. Results generated by disCoPThe results page can be reached by leaving the status page open for the duration of the prediction, orby following the link provided in the email.The email (Fig. 3) provides a job identifier together with the location of the prediction results page andthe text file with the results. Each submission is assigned a unique 14‐digits long identifier (Fig. 3, label1) that is shown at the top of the notification email (see Note 7). In the case of issues with thecompletion and/or contents of the prediction, the identifier can be used to trace the correspondingsubmission. The email message includes a direct link to the webpage with the results of the prediction(Fig. 3, label 2) and to the text file with the results (Fig. 3, label 3).Fig. 4. The disCoP prediction results webpage. The red D and green n denote the putative disordered residues and putativenon‐disordered (structured) residues, respectively. The corresponding putative propensity scores are provided directlyunderneath. Orange/yellow circles indicate important features of this page, discussed in the text.This results page (Fig. 4) includes a link to a text file results.csv with prediction results (Fig. 4, label 1)and a visualization of the predictions (Fig. 4, label 2) (see Note 8). The text file contains proteinidentifiers, sequences, binary predictions and propensity scores for each of the submitted proteinsequences. These data are in the comma‐separable CSV format to ease parsing. An example of the CSVformat results file is shown in Fig. 5. Each sequence is represented by three lines:Line 1. The protein name taken from the FASTA header provided by the user followed by theprotein sequence. The individual amino acids are comma‐separated to ease aligning themto the corresponding predictions listed in the two subsequent lines.Line 2. Binary predictions of disorder, where D denotes disordered residues and O denotedordered residues.Line 3. Propensity for disorder, ranging between 1 for high propensity to 0 for low propensity.Residues with propensity 0.5 are annotated as D in the second line.The visualization (Fig. 4, label 2) shows the binary annotations of the putative IDRs (using redhighlights) and putative ordered regions (green highlights) for each residue in the input protein chain.Each binary annotation is associated with the propensity score, which is provided directly underneath.The scores are in the range between 0 and 99, where residues with scores 50 are predicted in binaryas disordered.
,D,D,D,D,D,D,D,D,D,D,DdisCoP - 26,0.717,0.711,0.706,0.706,0.706,0.706,0.706Fig. 5. Example of the CSV format results file for the disCoP prediction. The example shows results produced for thechromatin accessibility complex 16kD protein (UniProt id: Q9V452).Fig. 6. Known intrinsically disordered and structured regions in CHRAC16 compared to disorder predictions. (Top panel)Structurally characterized regions are shown: two intrinsically disordered regions (red) and one structured region (blue).(Middle and bottom panels) Intrinsic disorder prediction scores given by SPINE‐D (cyan), DISOPRED (orange), DISOclust(green), MD (purple), and disCoP (pink, shown alone in the bottom panel) are shown, where values above 0.5 arepredictions of disorder and below 0.5 are prediction of structure. Hatch portions of the score lines indicate incorrectpredictions.
4. Case studyThe protein CHRAC16 is a component of the chromatin accessibility complex (CHRAC), formed byinteraction of CHRAC16 and CHRAC14 with the ATP‐utilizing chromatin assembly and remodeling factor(ACF) complex. The crystal structure of the CHRAC14/16 dimer has been determined [30], whichrevealed two disordered regions located at either terminus (Fig. 6, top panel). The N‐ and C‐terminalIDRs play a role in the ACF binding and modulating DNA binding affinity, respectively [30].Disorder predictions for CHRAC16 demonstrates the improvement of disCoP predictions relative to itscomponent predictions from SPINE‐D, DISOPRED, DISOclust, and MD. This is shown in Fig. 6 bycomparing the amount of incorrect predictions (hatch portions of the score lines) between disCoP andthe other four methods. For comparison, prediction scores from disCoP server’s CSV output file wereplotted along with prediction scores of the component predictors (Fig. 6, middle and bottom panels).The four component predictors of disCoP generally perform well for Drosophila CHRAC16 (Fig. 6,middle panel); SPINE‐D, DISOPRED, DISOclust, and MD predict 85%, 87%, 84% and 69% of residuescorrectly, respectively. Both SPINE‐D and DISOPRED predict disordered and ordered regions correctly,but predict the two disordered regions to be shorter than found experimentally. DISOclust and MDboth predict too much disorder, with MD predicting much of the structured region to be disordered. Incontrast, the disCoP prediction is highly accurate (Fig. 6, bottom panel), predicting 98% of residuescorrectly. Similar to SPINE‐D and DISOPRED, disCoP slightly under predicts disorder at the N‐terminusand C‐terminus, but only by two residues and one residues, respectively.Notes1. The FASTA format for the protein sequences is explained athttps://en.wikipedia.org/wiki/FASTA format. Briefly, each protein is represented by multiple lineswhere the first line that begins with “ ” followed by the name and description of the protein, andthe subsequent lines that provide the sequence using the 1‐letter amino acid encoding and with 20amino acids per line. Example follows IVPQKIRVHQFQEMLRLNRSAGSDDDDDDDDDDDEEESESESESDEThe disCoP server will also accept the second line that gives the entire protein sequence, i.e., theuser has the option of providing the sequence in one line or breaking it up into multiple lines.2. Up to five FASTA‐formatted sequences can be submitted at one time. Moreover, the programsused to implement the disCoP predictor limit the length of submitted protein sequences to therange between 26 residues and 1000 residues. These limits apply to both the text entry field andwhen uploading the file. Submissions exceeding either of these limits receive an error notificationfrom the server (“You entered 10 proteins. Up to 5 proteins allowed!” or “Input sequence is 1024amino acids long. The minimal allowed length is 26 amino acids and the maximal length is 1000.Please re‐submit your sequence.”) and prediction is disallowed. Requests with more than 5proteins have to be broken into multiple submissions each with 5 or fewer sequences; (also, seeNote 6). The users must combine the results from different submissions manually.3. Analysis of the predictions generated by disCoP benefits from examining the propensity scores inaddition to the binary predictions. High values of the propensity scores which are below the 0.5
4.5.6.7.8.threshold (and which consequently do not result in the binary prediction of IDRs) may suggestpresence of disorder if combined with other data. Benchmarks show that the threshold 0.5corresponds to the predictions with sensitivity of about 65% and low (15%) false positive rate,resulting in a rather conservative set of disorder predictions. This means that residues that werenot predicted as disordered based on the binary outputs and which have high propensity scoreshave elevated likelihood for disorder, but at higher levels of false positives.Rather than requiring an active browser connection for the duration of the entire prediction,notification of completed predictions are provided via the email address provided by the user.The http://biomine.cs.vcu.edu site includes several other predictors, such as (in alphabetical order)CONNECTOR [91], CRYSTALP2 [42], Cypred [39], DFLpred [51], DisCon [64], DisoRDPbind [71, 77],DMRpred [52], DRNApred [106], fDETECT [56, 58], fMoRFpred [105], funDNApred [1], hybridNAP[109], ILbind [33], MFDp [62], MFDp2 [60, 63], MoRFpred [13, 69], NsitePred [7], PPCpred [59],QUARTER [34, 96], RAPID [108], SSCon [107] and SLIDER [73].The http://biomine.cs.vcu.edu site utilizes the first‐come‐first‐serve queue. However, the numberof simultaneous submissions across all webservers (see Note 5) that are received from the same IPaddress is limited to three. Users who submit too frequently receive a message to resubmit afterone of their pending submissions is completed. This limit aims to equalize access to this resourceacross users by not allowing any one user to submit an excessive number of jobs that wouldseverely delay/block access for the other users.Both links to the results are based on the unique job identifier and they are not posted online. Thismeans that the other users of this webserver are unable to access the results, preserving privacy ofthe submission.Users should save the email and the links to the results. They can be accessed only via the links thatare provided in the notification email and on the results webpage.AcknowledgementsThis research was supported in part by the Robert J. Mattauch Endowment funds and the NationalScience Foundation grant 1617369 to Lukasz Kurgan.References1.2.3.4.5.6.7.Amirkhani A, Kolahdoozi M, Wang C et al. (2018) Prediction of DNA‐binding residues in local segments ofprotein sequences with Fuzzy Cognitive Maps. IEEE/ACM Trans Comput Biol BioinformAtkins J, Boateng S, Sorensen T et al. (2015) Disorder Prediction Methods, Their Applicability to DifferentProtein Targets and Their Usefulness for Guiding Experimental Studies. International Journal ofMolecular Sciences 16:19040Buljan M, Chalancon G, Dunker AK et al. (2013) Alternative splicing of intrinsically disordered regionsand rewiring of protein interactions. Current opinion in structural biology 23:443‐450Campen A, Williams RM, Brown CJ et al. (2008) TOP‐IDP‐scale: a new amino acid scale measuringpropensity for intrinsic disorder. Protein Pept Lett 15:956‐963Charon J, Theil S, Nicaise V et al. (2016) Protein intrinsic disorder within the Potyvirus genus: fromproteome‐wide analysis to functional annotation. Molecular Biosystems 12:634‐652Chen JW, Romero P, Uversky VN et al. (2006) Conservation of intrinsic disorder in protein domains andfamilies: II. Functions of conserved disorder. Journal of Proteome Research 5:888‐898Chen K, Mizianty MJ, Kurgan L (2012) Prediction and analysis of nucleotide‐binding residues usingsequence and sequence‐derived structural descriptors. Bioinformatics 28:331‐341
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Computational Prediction of Intrinsic Disorder in Protein Sequences with the disCoP Meta‐predictor Christopher J. Oldfield1, Xiao Fan2, Chen Wang3, A. Keith Dunker4 and Lukasz Kurgan1* 1Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA 2Department of
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